lodrein

Yes and no. The algorithm runs with historical student data and therefore only considers students using the bus system in order to compute the effects of school start times on transportation. On the other hand I think the district had planned to used the saved transportation money (~18 million/year) in order to help the most impacted parents (pre/post school care...)

I guess volunteer transit is always a positive thing for the parents and the districts. The algorithm was just trying to fulfil the objectives given by the district (costs, later students, fairness...). The compensations are what should happen after the algorithm: what do you do with the saved money, how do you help the most impacted parents? These negative effects were expected to disappear with the years as new parents start to choose the schools after the start times have changed (and therefore choose schools that can accomodate their schedules).

I am no specialist of children health, but my understanding is the following: I guess starting too early is never good for any child, but there is something specific with teenagers that makes it a lot worse. There is much more medical literature documenting this fact than for the younger kids. So yes, in a way, given the constraints of costs, the algorithm had to choose the lesser of two evils.

Yes incremental changes looks like a good solution. I think it is hard to do it with the delta minutes changed, as you could have dramatic consequences on the costs (the ripples on the transportation systems can be quite surprising, in that changing the time of one school can have a big impact on the number of buses needed). As to the number of student start time, the main question is how do you choose the schools that change first? How do you make sure that your changes are not interrupted in the middle for political reasons, which would be unfair for the first schools and maybe the whole system.

Also, it is an algorithmic challenge to find a "path" of good solutions that go step by step to the good final start times.

We talked about it at some point, and it seems like a good idea. There are two main issues with it: first it can be hard to do a step by step change that is (a) fair (how do you choose the first schools to change?) and (b) works for every step (make sure that the transportation costs do not explode in between).

That could be a way to do it, and Boston decided to do it all at once. I think their reasoning was the fairness of the process (it's hard/unfair to use beta testing on public schools). Also, it is such a hard change that maybe it is easier to do it all at once. You may also want to consider the political implications that are very important when making this kind of decisions.

Thanks for sharing!

I am sure more engagement is always a good thing! One of the tricky tasks of BPS is to make sure that this engagement is "balanced", i.e., everyone's voice is equally heard. And you can imagine how hard that may be in practice. For example, Boston Public Schools had this survey asking people for their preferences on the start times. But most of the answers from these survey came from schools with less economically disadvantaged families. One way to do it is to call the families directly or have a lot of local meetings. But it is still a complicated problem.

per transported students. The transportation cost are more than 100 million per year.

This can happen and did happen before. Maybe being suspicious of an algorithm is not a bad things, we should have extremely high standards for them ! My hope is that some people will just have a better view of how complicated this problem is, and be able to construct their own opinion from there. And this would already be something I would be proud of!

It's true that it was fast, maybe too fast, although the district was planning this conversation for more than 10-20 years. But even if it has not been implemented, the debate itself may have been worth the trouble. The complex problem of start time choice is an incredibly deep and interesting question, and so is the interface between algorithms and public policy.